Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/71198
Title: Off-road obstacle detection with robust parametric modeling of the ground stereo geometry
Authors: Kodagoda, S. 
Dong, G.
Yan, C.H.
Ong, S.H. 
Keywords: Autonomous vehicles
Geometric modeling
Hough transforms
Piecewise linear approximation
Scene analysis
Stereo vision
Issue Date: 2009
Source: Kodagoda, S.,Dong, G.,Yan, C.H.,Ong, S.H. (2009). Off-road obstacle detection with robust parametric modeling of the ground stereo geometry. Proceedings of the IASTED International Conference on Robotics and Applications : 343-350. ScholarBank@NUS Repository.
Abstract: Autonomous navigation in off-road environments presents many challenges in contrast to the more conventional, urban environments. Unstructured surroundings, non-uniform visual cues and lack of prior knowledge about the scene complicate the design of even basic functionalities such as obstacle detection. This paper presents a stereo vision based ground geometry modeling and obstacle detection algorithm that is well suited for cross-country navigation. Our mathematical analysis shows that the "ν-disparity" method is inadequate for accurate terrain modeling under vehicle pose variations; to compensate for this shortcoming, we propose a novel extension to the original algorithm. As the preliminary step of this extension, lateral gradient of the ground disparity is estimated using histogram analysis. This information is subsequently propagated to a modified "ν-disparity" algorithm that models the longitudinal terrain disparity variation. The effectiveness of this two-phase ground modeling technique for obstacle detection is demonstrated with empirical results.
Source Title: Proceedings of the IASTED International Conference on Robotics and Applications
URI: http://scholarbank.nus.edu.sg/handle/10635/71198
ISBN: 9780889868137
ISSN: 1027264X
Appears in Collections:Staff Publications

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